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MKT 700 Business Intelligence and Decision Models

MKT 700 Business Intelligence and Decision Models. Week 6: Segmentation and Cluster Analysis. Clusters and Segments (Chap 10). Differences between clusters and segments Learning segmentation Dynamic segmentation. Status Levels and Segments. Consumer Segmentation Taxonomy.

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MKT 700 Business Intelligence and Decision Models

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  1. MKT 700Business Intelligence and Decision Models Week 6: Segmentation and Cluster Analysis

  2. Clusters and Segments (Chap 10) • Differences between clusters and segments • Learning segmentation • Dynamic segmentation

  3. Status Levels and Segments

  4. Consumer Segmentation Taxonomy • Family life cycle (stage in life) • Lifestyle (personal values) • Product usage/loyalty • Preferred communication channel • Buying behaviour

  5. Data Sources for Segmentation • Internal • Transactions • Surveys & Customer Service • External (Data overlays) • Lists • Census • Taxfiler • Geocoding

  6. Geo-Segmentation in CDA Birds of a feather f___k together… • Environics (Prizm) • http://www.environicsanalytics.ca/prizm-c2-cluster-lookup • Generation5 (Mosaic) • http://www.generation5.ca • Manifold: • http://www.manifolddatamining.com/html/lifestyle/lifestyle171.htm • Pitney-Bowes (Mapinfo) • http://www.utahbluemedia.com/pbbi/psyte/psyteCanada.html

  7. B2B Segmentation Taxonomy • Firm size (employees, sales) • Industry (SIC, NAICS) • Buying process • Value within finished product • Usage (Production/Maintenance) • Order size and Frequency • Expectations

  8. Clustering • Measuring distances (differences) or proximities (similarities) between subjects

  9. Measuring distances(two dimensions, x and y) A B C

  10. Measuring distances(two dimensions) dac2 = (dx2 + dy2) A B C dac2 = (di)2 dac = [(di)2]1/2

  11. Measuring distances(two dimensions) D(b,a) A B D(a,c) D(b,c) C

  12. Distances between US cities

  13. Cluster Analysis Techniques • Hierarchical Clustering • Metric, small datasets

  14. SPSS Hierarchical Clusters Dendogram

  15. SPSS Multidimensional Scaling (Euclidean Distance) 1 2 Atlanta .9575 -.1905 Chicago .5090 .4541 Denver -.6416 .0337 Houston .2151 -.7631 Los_Angeles -1.6036 -.5197 Miami 1.5101 -.7752 New_York 1.4284 .6914 San_Francisco -1.8925 -.1500 Seattle -1.7875 .7723 Washington 1.3051 .4469

  16. Euclidean distance mapping

  17. Cluster Analysis Techniques • Hierarchical Clustering • Metric variables, small datasets • K-mean Clustering • Metric, large datasets • Two-Step Clustering • Metric/non-metric, large datasets,optimal clustering

  18. Cluster Analysis Techniques See Chapter 23, SPSS Base Statistics for description of methods

  19. Two-Step Cluster Tutorials • SPSS, Direct Marketing, Chapter 3 and 9 Help  Case Studies  Direct Marketing  Cluster Analysis File to be used: dmdata.sav • SPSS, Base Statistics, Chapter 24 Analyze  Classifiy  Two-Step Cluster File to be used: Car_Sales.sav Help: “Show me”

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